When it comes to the talk of technological advancements, artificial intelligence will always be one of the most revolutionizing technologies that are present in many industries. Artificial Intelligence is defined as the theory and development of computer systems to be able to perform human tasks like visual perception, speech recognition, and even decision-making.

The first AI program can be traced as early as the 1950s and since then, it’s been continuously developed. Right, now we can already see how powerful AI is and how widely present it is in many industries like gambling.

Artificial Intelligence has always been heavily acquainted with gaming including gambling. This is more specifically for online gambling or online casino gaming. AI has been playing an important role in different aspects when it comes to online gambling. Here are some of the aspects of online gambling that is benefiting from Artificial Intelligence.

• Getting New Customers

Online casinos are constantly trying to find ways to get new customers. The competition is just tough because there are now so many online casino operators out there nowadays and all operators have their ways of enticing people like giving great casino bonuses.

Players would usually always prioritize to choose a casino bonus that will be beneficial for them and this is why casinos must reach people who will be interested in their offers. And so, advertising plays an important part here and casino ads are usually more visible to people with casino gaming history. This is something that operators learn by using AI-powered algorithms.

• Player Verification

Security is extremely important to casino operators and they must screen their players well. The verification process allows them to make sure that people signing up on their site are real people. For some casinos, their verification process includes face-recognition as well.

Aside from the verification process, they also use AI tech to keep track of suspicious activities. The AI tech that they use on this can identify whether suspicious activities or fraudulent behaviors are going on. This helps ensure that the personal information of their customers are players are secure.

• Smart Data

Ever wonder why it is that whenever you log in to your casino account, you always seem to be presented with games that you are likely to play? AI technology is also responsible for that. The AI algorithm determines what games you might enjoy based on the other games that you’re playing. This way, online casinos get to ensure that what they are offering to their customers appear customized.

Just remember that any AI program functions based on the data it gets. It may get data from the player’s information and activities. Whatever the AI algorithm will do is based on its programming. Once data is gathered, that’s when the algorithm functions and knows if something is suspicious or this is something that a player will find interesting.

• Virtual Reality

Virtual Reality has been the talk for a while now in the gaming and gambling sector. There are already online casino games that you can play with a VR device. Playing with VR casino games lets you experience realistic online casino gaming and this is also made possible because of AI technology.

When playing any VR games, the result of the interaction you make with any object in the virtual world is the product of AI. How your virtual environment reacts towards your actions is the AI functioning. If you are playing an online casino game against a computer, that’s also AI. It’s the computer functioning like a real player and the actions that your computer opponent take is also the result of compiling data from other players.

• Combatting Gambling Addiction

Many online casinos nowadays are very much cooperative when it comes to combatting gambling addictions in different countries as it is a very serious issue. AI helps casinos combat gambling addiction by analyzing player activities. There are AI programs that could detect problematic gambling patterns or behaviors.

Problematic behavior can be detected in an early stage and it could alert online casinos. If proven that there seems to be something problematic when it comes to the activities of a player, then the online casino could suspend the player’s account. The operator could also give options to the player when it comes to how they could help fight gambling addiction.

AI technology is continuously developed. AI programs are consistently learning and are now function more human-like. Just imagine how far technology would go because of this and this could make online casino gaming more realistic in the future. It is understandable why many are fearing the power of AI, but its benefits are countless.

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The pandemic has changed the world, accelerating the pace of digital transformation and forcing businesses to find new efficiencies — not only in cost cutting but in decision making.

That’s why business leaders love AI, because it can help them make better and faster decisions by providing quicker and more accurate insights. It can tell them what customers really want — and what they want right now.

There is a problem though: Consumers don’t love AI. In one recent study, only 25% said they would trust an AI decision more than one made by a human, and less than 30% were comfortable with businesses using AI to interact with them.

Consumers are skeptical about AI because they don’t understand it. Many are scared by what they read and hear and not all their fears are misplaced. 

Unintentional built-in bias leads algorithms to make decisions, like credit denials, that unfairly discriminate against some minority consumers. It’s also probable that jobs will be lost to AI. What industry does to mitigate both of these problems will be key to maintaining consumer trust. 

Many business leaders attempt to impress customers merely by devising an “AI strategy” or building an “AI system” — and trumpeting it loudly. But customers are put off by this approach. 

What consumers value is the convenience, speed and choice delivered by AI working behind the scenes when they’re doing things like shopping or seeking health advice. 

They’re impressed by better products and services, greater convenience and better value for money, all of which AI delivers. The disconnect means business leaders need to think about their AI efforts differently, and spend more time educating consumers on how AI benefits them.

Not to be denied: AI is doing great things

Few shoppers realize the overnight arrival of their online purchases depends on AI algorithms getting items packed and shipped expeditiously. How many grasp that AI enables a customer service agent or chatbot to make quick-fire responses to their queries and requests? Few probably understand AI is locating the nearby Lyft driver within seconds of a tap on a smartphone.

The workings of AI are mostly unseen in the financial industry, where they very much benefit both institutions and customers. Take fraud detection. Some earlier protections were simple, blunt instruments. 

Sure, they stopped fraudulent transactions, but they also blocked many legitimate ones (“false positives”), often resulting in terrible customer experiences. Today, AI-based platforms pinpoint fraud faster and better, reducing false positives by more than half. That equals more legitimate transactions and fewer unhappy customers.

Within organizations, AI is eliminating repetitive tasks and enabling creativity. Business leaders should be asking, how much drudgery can AI push out of the system so my employees can do more creative work?

Diversity and discretion

What lessons can business leaders draw from all this? One is to tackle the bias issue — the danger that flawed algorithms make decisions unfairly, disadvantaging consumers. 

The first step is to understand that the bias isn’t in the AI but in the data, whether it’s data used initially to train the algorithm or data entered to generate insights. The best way to minimize bias is through diversity — finding and using the widest possible range of datasets. Not just quantity but variety. 

In human interaction, we fight bias by talking to people with different perspectives, by working with people that challenge and question the status quo. The same approach needs to be taken to the data we use to inform our algorithms.

Different groups of business stakeholders need different talking points when it comes to AI. Analysts and investors may be impressed with tales of tech capabilities, but customers just want to hear about better services or products, and that their data is being strongly protected and ethically used. 

We’re more likely to build trust by flat out saying, “We’re not going to compromise your data.” 

Business leaders need to understand AI’s limitations. AI is a tool, not a product, a strategy or a business model. Powerful it may be, but AI is only assisting us in doing the things we’ve always strived to be: smarter, faster, more efficient and, hopefully, more trustworthy

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Undercover operatives and agents play a crucial role in preventing and safeguarding victims from the most serious crimes, including terrorism. In order to gain the trust of those under investigation, there are occasions where they need to participate in criminality themselves. This is a long-standing capability which remains critical for national security.

The Covert Human Intelligence Sources Bill provides certainty to public authorities already using this critical capability and confirms a common set of safeguards by which they are bound, including compliance with Human Rights.

Debt of gratitude

Security Minister James Brokenshire said: “We owe a huge debt of gratitude to the men and women who put themselves in often dangerous situations in order to protect our national security and keep the public safe. In the course of this vital work, it may be necessary for agents to participate in criminal activity in order to gain the trust of those under investigation. This is a critical capability and subject to robust and independent oversight. It’s important that those with a responsibility to protect the public can continue this work, knowing that they’re on a sound legal footing to do so.”

Ken McCallum, director general of MI5, stated: “Throughout MI5’s history, human agents have played a critical role in helping to protect the UK from terrorist threats and hostile activity by states. Since March 2017, MI5 and Counter-Terrorism Policing have together thwarted 27 terror attacks. Without the contribution of human agents, be in no doubt that many of these attacks would not have been prevented. In some situations, it’s both necessary and proportionate to authorise agents to be involved in some managed level of criminal activity in order to win or maintain the trust of those intent on harming the UK and gain the critical information needed to save lives.

This power, carefully used and independently overseen, is vital such that we can continue to meet our duty to keep the public safe.”

Express power

DAC Graham McNulty, Covert Human Intelligence Sources lead at the National Police Chiefs’ Council, explained: “We welcome the creation of an express power which will ensure policing can continue to deploy this vital tactic against the most harmful offenders. From terrorists to violent gangs and organised criminals who seek to harm the most vulnerable in society, this Government Bill will help us to disrupt their activities and keep our communities safe.”

Lynne Owens, director general of the National Crime Agency, commented: “We lead the UK’s fight to cut serious and organised crime, focusing on the most determined criminals who dominate communities through violence linked to drugs and firearms supply, who abuse the vulnerable and who threaten the UK’s economic security and institutions. Law enforcement has long used Covert Human Intelligence Sources to help thwart the most serious criminal threats to our nation and its partners.”

Owens went on to state: “Only when it’s absolutely necessary and proportionate will we authorise our Covert Human Intelligence Sources to be involved in a limited form of criminal activity. This is done with great care and scrutiny. Without this capability we would not be able to bring to justice criminals and their groups who conspire to harm the UK and its citizens. We welcome this new legislation which puts this crucial capability on a firm foundation.”

Robust oversight

Robust independent oversight is provided by the Investigatory Powers Commissioner, namely Sir Brian Leveson, who carries out regular inspections and publishes an Annual Report on the findings for public consumption.

This capability is supported by the courts, with the Investigatory Powers Tribunal (in its recent supportive judgment on the use of Covert Human Intelligence Sources participation in criminal conduct) noting that: “The policy… has been exercised with scrupulous care by the Security Service so as to discharge its essential functions in protecting national security, while also giving proper regard to the Human Rights of persons who may be affected by the activities of agents.”

The Investigatory Powers Commissioner has also commented that, with regards to MI5, “in every case that we examined… the activity authorised was proportionate to the anticipated operational benefits”.

The public authorities that will be authorised under the Bill are the UKIC, the police service, the National Crime Agency, the Home Office (immigration and borders functions), Her Majesty’s Revenue and Customs, HM Forces and their police, the Ministry of Justice (HMPPS), the Competition and Markets Authority, the Environment Agency, the Financial Conduct Authority, the Food Standards Agency, the Gambling Commission, the Medicines and Healthcare Regulation Authority and, last but not least, the Serious Fraud Office.

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We are living through one of the most challenging and devastating health crises in living memory. This year has brought untold loss of life and livelihoods, the true worldwide repercussions of which are still to be seen. The COVID-19 pandemic has also altered the global business landscape, accelerating the pace and volume of data created through increased remote working and digital transacting, and fast shifting the economic realities for all business leaders. Against this backdrop, technology – artificial intelligence (AI), in particular – now has an even bigger role to play in helping organizations and countries to adapt, keep us safe and improve how we live and work.

But the thirst and drive to innovate with these new technologies at speed must be balanced with the need to carefully build consumer trust in those same innovations. Bringing consumers on this journey will be key. A new report from Longitude explores this precise balance between engendering trust in technology and fostering AI innovation. This is a topic that I am very passionate about and one that I spoke on at Davos this year, so I want to explore some of the report’s findings in more detail here.

Transparency is everything

Until recently, there has been too much focus on what AI can do and not how it does it. Today’s organizations must be able to demonstrate that their systems and algorithms are responsible, fair, ethical and explainable. In a word, that their AI is trustworthy.

High-profile cases of misuse of AI by global technology firms have dented consumer trust in AI. The subsequent fallout has also raised greater global awareness of the broader issues around the use of data and our personal information.

The result? Trust in technology can no longer be assumed – it must be earned. In this sense, organizations must think of their technology as ‘guilty until proven innocent’. The onus is on them to proactively demonstrate the responsible use of their technology to the world and to be prepared to explain and justify decisions made by those systems when required. Here we have the right to meaningful information about the logic, significance and envisaged consequences of automated decisions or what is also called ‘the right to explanation’, as laid out in the EU’s General Data Protection Regulation (GDPR). Businesses must consider how they apply these technologies – only using personal information when it is needed and with the user’s consent. By building these principles into AI as it is developed, businesses can ensure that it is ethical and transparent from the outset.

We are already seeing the impact of this transition to ethical AI. A recent Capgemini study found that 62% of consumers placed more trust in a company whose AI was understood to be ethical, while 61% were more likely to refer that company to friends and family, and 59% showed more loyalty to that company. Those who openly communicate in this way about how their technology works are more likely to be trusted by consumers to use AI to its full potential.

AI is our best problem solver

The COVID-19 pandemic has accelerated digitalization at a rate we could have never imagined. This has created a volatile environment with a plethora of challenges to overcome and opportunities to exploit. In the payments industry, we have had the challenge of protecting consumers and businesses against an explosion in cyberattacks and fraud – for example, our NuData technology, which verifies users based on their inherent behaviour, has seen attacks become more sophisticated, with one in every three attacks now emulating human behaviour. Account creation attacks, where bad actors create fake accounts for subsequent fraudulent use, have increased by 500% during the pandemic, compared to the same period in 2019 – one global retailer experienced a 679% increase in suspicious account creations alone. Overall, global fraud rates have hit a near-20-year high, according to the latest PwC figures, with 47% of companies reported to have experienced fraud over the past two years.

It would have been impossible to maintain our defences without the implementation of AI on our network. It is, and will continue to be, a vital part of adapting to and securing this new world. As AI becomes more powerful and pervasive, we must put systems in place to ensure that it is developed and deployed ethically.

Consumer driven, consumer focused

Consumers create a huge amount of data. By 2025, we will be creating an estimated 463 exabytes every day. And that is only going to increase – the oft quoted formula is that 90% of the data that has ever been created was created in the last two years. AI-driven systems have been invented to help turn some of this information into recognizable benefits for the people who create that data – making our lives work for us.

But AI is a technical and complicated tool. The trust that is needed for it to be most effective will come when consumers see and feel its real-world benefits in action. In this sense, trust can be a key differentiator – a competitive advantage for businesses. Only those who are trusted to operate AI will be able to maximise the benefits of its value-added services in years to come. Not only can AI deliver safety for consumers online or revolutionise their shopping experiences; it is also revolutionising farming as well as giving the environment a new lease of life. For those that get it right, the possibilities are endless.

The big picture

At times of such uncertainty, it can be difficult to look too far ahead. But now is the time for business leaders to take a step back and look at the bigger picture. The landscape has changed, and that change is permanent. Our digital futures have been brought forward and society will continue to demand higher levels of transparency in the way that AI is used to solve new challenges.

Responsible development of, and engendering trust in, technology will be crucial to business success in the ‘next normal’ – but more importantly, to building a world that is more prosperous and more equal for all.

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Artificial intelligence (AI) advancements are showing no signs of slowing down. With new developments coming on the market daily, there’s now a decent amount of options for businesses to get a helping hand with many tedious and administrative tasks.

From the marketing perspective, the area with the largest room for productivity gains come from the creative side. Working with hard-to-manage visual assets, which have very little “data” associated with it comes with a wide set of challenges. But, as AI progresses, I anticipate we’ll see major advancements in how AI can help marketing teams make the most of their visuals.

Here are 4 predictions on how AI will enhance visual marketing.

Find-and-Replace Capabilities

For many organizations, change is a constant — branding updates, acquisitions, mascot updates, product launches, etc., lead to small, but continuous design changes. This can be a major pain for marketing and creative teams, who are responsible for making those updates across all pieces of content — website pages, PDFs, print materials. Not only is this incredibly tedious, it also leaves the organization susceptible to human error that can lead to market confusion and brand deterioration.

As AI begins to understand design files, like InDesign and Photoshop, AI models will be able to learn to make these kinds of updates instantaneously. Similar to the find-and-replace feature available for Word documents, AI will be able to recognize a new version of an asset — for example, a logo — and replace the previous version across all design files. These revisions could then automatically be replaced in the organization’s digital asset management system as a new version.

Outdated Collateral Identification

Managing discontinued products is another thing that can take up a significant amount of time for marketers. While this is often most associated with the retail industry, it’s also applicable across many more industries. The hospitality, technology and manufacturing industry, for example, all handle their own form of closures, discontinuations or “sunsets” of products. When a product is discontinued, so begins the painful task of hunting down and removing the product from each and every piece of collateral. It can be tireless and thankless work, but very necessary.

In the not too distant future, it’s likely that AI models will be trained to identify and flag pieces of collateral that contain discontinued items. Using the advancements already being made with customized AI capabilities, AI models would understand how to recognize the product by its physical characteristics, as well as associated SKU number, name and price. When a product is labeled as discontinued, the model could then quickly scan all advertisements, catalogues, pricing sheets, etc., and notify the marketing team of which pieces need to be updated.

Image Personalization

Content personalization is already a well-known application of artificial intelligence — one that’s being taken advantage of by many marketers today. Using customer data, history and preferences, AI models can predict which pieces of content will resonate with each individual customer and serve up that content. While this is applicable for blog posts, videos, ebooks and other text-based content, AI is still yet to offer content personalization for images.

This will likely change in the coming years. Artificial intelligence has had a huge impact on images as of late, enabling the auto-tagging of images with critical information like object, colour and text recognition. Using these additional pieces of information, there’s no reason that an AI model couldn’t surface images in each customer’s preferred style, colors, etc. When connected to a digital asset management system, it would also open up the AI model’s ability to select from each and every image available in the organization.

Asset Suggestions

Suparman Widjaja, technology manager for Verizon’s creative marketing group, shared his insights on how artificial intelligence could help optimize campaign success.

“There are so many opportunities to use artificial intelligence in our industry — specifically, to expand into more marketing and creative use cases. For example, AI could help our team predict the success of certain assets based on campaign goals, and identify assets that would be more successful based on campaign attributes, target segments, season, etc. As we measure the outcomes of these campaigns, we could feed the output back into the AI engine to continually improve its accuracy.”

He continued, “I’d like to be able to train models to analyze our copy and assets to help identify a strategy for each campaign. These kinds of output, while unable to replace human creativity, can significantly reduce the time we spend deciding on the focus for our creative work.”

Artificial Intelligence for Digital Marketing

While artificial intelligence still has a long way to go, there are plenty of ways to infuse AI into your digital marketing strategy today. Download your copy of 8 Ways AI Can Power Your Digital Strategy to see how you can get started.

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Developed by the Office for Artificial Intelligence (OAI) in collaboration with the World Economic Forum (WEF) Centre for the Fourth Industrial Revolution, Government Digital Service (GDS), Government Commercial Function and Crown Commercial Service, the document seeks to enable public bodies to buy AI systems in a more confident and responsible manner.

The guidance for procurement, which refers mostly to the use of machine learning and was created with the input of a range of stakeholders from industry, academia and government departments, was initiated through the WEF’s Unlocking Public Sector AI project. It follows a previous guide to using AI in the public sector by the OAI and the Government Digital Service, released in January 2020.

As part of the initiative to help managers in government in terms of decisions around buying AI systems, the OAI brought stakeholders together to develop AI Procurement in a Box, a toolkit to help procurement professionals develop their approaches to AI procurement in the UK and abroad.

The document provides an overviews of what themes should be considered when assessing the possibility of buying an AI system and is intended for use alongside other previously published policy and advice, in areas such as data ethics and open data standards.

Including procurement in a strategy for AI adoption is one of the 10 items of advice listed in the document. Here, buyers are told they should be using procurement strategically to encourage AI adoption across government, take advantage of economies of scale in the public sector and set up networks to learn from work done elsewhere. Setting up diverse teams to ensure understanding of the interdependent disciplines that AI technologies incorporate is also part of the advice.

The guidance noted that buyers should conduct a data assessment before starting a procurement process for an AI system, and consider asking questions such as whether relevant data will be available for the project. Benefits and risks of AI deployments should also be considered, as well as asking whether AI is the right solution for the problem at hand.

Effective market engagement is another piece of advice provided in the guidance. According to the document, procurement professionals should be careful with actions that could introduce hurdles for certain under-represented groups, including small and medium-sized enterprises (SMEs), as well as voluntary, community and social enterprises suppliers from competing. Technical and ethical shortcomings of AI deployment should also be considered, the document says.

Carefully writing the project requirements to help suppliers understand what is required is another part of the guidance, which suggests exploring different routes to market to acquire AI systems – for instance, Innovation Partnerships, the GovTech catalyst and the Crown Commercial Service’s AI Dynamic Purchasing System.

Developing a plan for governance and information assurance is another part of the advice provided to AI buyers in the pubic sector, as well as avoiding risks such as supplier lock-in. The need to consider lifespan testing for AI deployments, rather than one-time solutions, is also stressed in the guidance.

In addition, the document raises specific considerations to be addressed during processes of acquisition of AI systems. These include preparation and planning, selection, evaluation and awards, as well as what to keep in mind at the stage of contract implementation and ongoing supplier management, including knowledge transfer and training, and end-of-life processes.

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Not long ago US President Donald Trump quoted that, “Continued American leadership in AI is of paramount importance to maintaining the economic and national security of the United States and to shaping the global evolution of AI in a manner consistent with our Nation’s values, policies, and priorities.”

President Donald J. Trump launched the American Artificial Intelligence Initiative, the Nation’s strategy for promoting American leadership in AI, by signing Executive Order 13859 in February 2019. Reportedly, the American AI Initiative focuses the resources of the Federal Government to support AI innovation that will increase prosperity, enhance national security, and improve quality of life for the American people. This national strategy for promoting United States leadership in AI emphasizes the following key policies and practices:

Invest in AI research and development

The United States has decided to promote Federal investment in AI R&D in collaboration with industry, academia, international partners and allies, and other non-Federal entities to generate technological breakthroughs in AI. President Trump called for a 2-year doubling of non-defense AI R&D in his fiscal year (FY) 2021 budget proposal, and in 2019 the Administration updated its AI R&D strategic plan, developed the first progress report describing the impact of Federal R&D investments, and published the first-ever reporting of government-wide non-defense AI R&D spending.

Unleash AI resources

The United States is adamant on enhancing access to high-quality Federal data, models, and computing resources to increase their value for AI R&D, while maintaining and extending safety, security, privacy, and confidentiality protections. The American AI Initiative called on Federal agencies to identify new opportunities to increase access to and use of Federal data and models. In 2019, the White House Office of Management and Budget established the Federal Data Strategy as a framework for operational principles and best practices around how Federal agencies use and manage data.

Remove barriers to AI innovation

The United States is making efforts in reducing barriers to the safe development, testing, deployment, and adoption of AI technologies by providing guidance for the governance of AI consistent with our Nation’s values and by driving the development of appropriate AI technical standards. As part of the American AI Initiative, The White House published for comment the proposed United States AI Regulatory Principles, the first AI regulatory policy that advances innovation underpinned by American values and good regulatory practices. In addition, the National Institute of Standards and Technology (NIST) issued the first-ever strategy for Federal engagement in the development of AI technical standards.

Train an AI-ready workforce

The United States is empowering current and future generations of American workers through apprenticeships; skills programs; and education in science, technology, engineering, and mathematics (STEM), with an emphasis on computer science, to ensure that American workers, including Federal workers, are capable of taking full advantage of the opportunities of AI. President Trump directed all Federal agencies to prioritize AI-related AMERICAN ARTIFICIAL INTELLIGENCE INITIATIVE: YEAR ONE ANNUAL REPORT – apprenticeship and job training programs and opportunities. In addition to its R&D focus, the National Science Foundation’s new National AI Research Institutes program will also contribute to workforce development, particularly of AI researchers.

Promote an international environment supportive of American AI innovation

The United States is working on engaging internationally to promote a global environment that supports American AI research and innovation and opens markets for American AI industries while also protecting our technological advantage in AI. Last year, the United States led historic efforts at the Organization for Economic Cooperation and Development (OECD) to develop the first international consensus agreements on fundamental principles for the stewardship of trustworthy AI. The United States also worked with its international partners in the G7 and G20 to adopt similar AI principles.

Embrace trustworthy AI for government services and missions

The United States is readily embracing technology such as artificial intelligence to improve the provision and efficiency of government services to the American people and ensure its application shows due respect for our Nation’s values, including privacy, civil rights, and civil liberties. The General Services Administration established an AI Center of Excellence to enable Federal agencies to determine best practices for incorporating AI into their organizations.

America’s strong innovation ecosystem, fueled by strategic Federal investments, visionary scientists and entrepreneurs, and renowned research institutions, has propelled United States global leadership in AI. However, continued leadership is not predetermined. Maintaining America’s preeminent role in AI can only be realized by continually building upon our progress and pursuing a strategic, forwardlooking approach in partnership with industry, academia, nonprofit organizations, and other non-Federal entities.

Global leadership in AI matters. With the United States in the lead — together with like-minded allies — we will shape the trajectory of AI development for the good of the American people — enriching our lives, promoting innovation, fostering trust and understanding, and ensuring our national defense and security.

Importance of partnerships to maintaining United States leadership in AI

In all areas of strategic emphasis, partnerships and collaboration with academia, industry, nonprofit organizations, civil society, other non-Federal entities, and international partners and allies are of growing importance. Concurrent advances across government, universities, and industry mutually reinforce an innovative, vibrant American AI sector. Public-private partnerships enable strategic leveraging of resources, including facilities, datasets, and expertise. These partnerships also accelerate the transition of research innovations to practice, by leveraging industry expertise to turn open and published research results into viable products and services in the marketplace for economic growth.

In the education and workforce area, partnerships are enhancing education and training for next-generation researchers, technicians, and workers, so that all can contribute to the 21st century economy. Partnerships with nonprofit organizations and civil society can help address important societal challenges arising from technological developments. The Nation also benefits from relationships between Federal agencies and international partners who work together to address key challenges. AI partnerships with allies and partners represent one of our sources of strategic competitive advantage.

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The potentials of Artificial Intelligence have extended its reach across several platforms. Even social media platforms like Facebook and LinkedIn are using AI to make sense of the pool of human data. Around 3 billion people use social media across various countries creating data in volumes one can only imagine. And this is where AI is introduced to harness the true worth of voluminous data.

In this era of social media, whether it is about reaching out to new clients or nurturing existing business or personal relationships, one can easily cling onto a site like Facebook, Instagram, or LinkedIn. Through such platforms, people can become an integral part of far off society and community.

In terms of business, effective social media management helps enhance a brand’s strength and increase social media conversations. It is an efficient way of engaging millions of social media users. AI helps analyze voluminous data to identify trending topics, hashtags, and patterns to understand user behavior.

Such innovative algorithms can keep a check on millions of unstructured user comments or data to understand crisis situations or trends to provide a personalized experience. With effective segmentation, technology can help organizations provide content based on online activity and demographics. Many social networking sites have acquired AI businesses to move to the next level.

In the contemporary market, a variety of companies that offer online marketing services are exploring new ways to use social media with AI as well. They have started using AI to identify new demographics to target based on previous conversions. These AI tools rely on predictive analytics algorithms, which can extrapolate information on all known users in a given social network.

AI can also recognize images and help identify consumer behavior patterns. AI-empowered software recognition tools can help gather actionable insights to understand the shift in user patterns through millions of images posted on social media.

With the sheer amount of images being posted every minute, it would be very difficult for a person to notice an opportunity like but with AI the task can be carried out efficiently.

At Facebook, where there are more than two billion users, it is using artificial intelligence to flag posts automatically that show expressions of suicidal thoughts for human moderators to review. Moreover, the social media platform can enhance its program to allow human moderators to review 20 times more suicidal posts, and Facebook is sending its suicide prevention materials to twice as many people.

Over, Microsoft owned professional networking site LinkedIn, AI’s subset machine learning is being used for almost all its products. With almost seven million open candidates, LinkedIn offers the biggest pool of candidates for a recruiter to contact and connect with the highest response rate. LinkedIn uses algorithms with the capability to predict users who are possibly the best fit for the role. Using AI algorithms, LinkedIn can highlight candidates who are most likely to respond or seeking new opportunities.

Furthermore, Twitter recently launched an update to its service using AI that crops an image using face detection or creating a thumbnail from an entire image. With neural networks, Twitter can decipher which part of the image user would be interested in.

As we can see through social media platforms AI is gaining prominence. While AI can never replace human interaction, it can assist them in making things more productive and efficient.

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AI programming is an elevation of technology that has brought efficiency and optimum benefits to different company’s operations and people’s lives. AI has brought another level of smart technology to different industries and the prospects of its potential still grow with the expectation that it would reach human intelligence. This is because developers are willing to explore, experiment, and implement their capabilities to satisfy more of the human and organization necessities. After all, necessity is the mother of invention.

Therefore, here is the list of top 5 programming languages that are in trend for AI developments in 2020.

Python

Python is an Interpreted language which in lay man’s terms means that it does not need to be compiled into machine language instruction before execution and can be used by the developer directly to run the program. This makes it comprehensive enough for the language to be interpreted by an emulator or a virtual machine on top of the native machine language which is what the hardware understands.

Python offers the least code among others and is in fact 1/5 the number compared to other OOP languages. No wonder it is one of the most popular in the market today.

Python has Prebuilt Libraries like Numpy for scientific computation, Scipy for advanced computing, and Pybrain for machine learning (Python Machine Learning) making it one of the best languages For AI.

R

R is a multi-paradigm language that can be called a procedural one, much like Python is. It can also support object-oriented programming, but it is not known for that feature as much as Python is.

R is considered to be a statistical workhorse, more so than Python. Once you start learning, you will understand that statistics form the base of machine learning and AI too. This means that you will need something which can suit your needs, and R is just that. R is considered to be similar to SAS and SPSS, which are other common statistical software. It is well suited for data analysis, visualization, and statistics in general. However, it is less flexible compared to Python but is more specialized too.

R is an open-source language too. This does not simply mean that it is free to use, for you – it also implies that you will have a lot of support when you start to use it. R has a vast community of users, so there is no dearth of help from expert practitioners if you ever need any.

Java

One of the best things about Java is Java Virtual Machine Technology. This technology allows developers to build a single app version that will run on all Java-enabled computing platforms. Major strengths of this programming language are as following: maintainability; portability; transparency.

AI is closely connected with search algorithms, genetic programming, and the use of artificial neural networks. Java in the artificial intelligence sphere may be more than useful. Programming AI in Java has many benefits: easy use, debugging ease, simplified work with large-scale projects, facilitated visualization, better user interaction. Another reason for programming AI in Java is the incorporation of Swing and SWT (the Standard Widget Toolkit). These features make graphics and interfaces look appealing and sophisticated.

Another reason for using Java in AI programming is the vast amount of tutorials on the Internet. Just type “how to program artificial intelligence in Java” and you’ll get a lot of pages to choose from. Java is versatile. It’s used for making multi-robot systems, sensor networks, and machine learning suites.

Scala

Just like Java, Scala belongs to the JVM family. Scala is a fairly new language in the AI space but it’s finding quite a bit of recognition recently in many corporations and startups.

It has a lot to offer in terms of convenience which is why developers enjoy working with it. Also, ScalaNLP, DeepLearning4j, etc are all tools and libraries that make the AI development process a bit easier with Scala. It’s good for projects that need scalability and combines the strengths of Functional and Imperative programming models to act as a powerful tool that helps build highly concurrent applications while reaping the benefits of an OO approach at the same time.

Scala provides good concurrency support which helps with projects involving real-time parallelized analytics. It has a good open source community when it comes to statistical learning, information theory, and Artificial Intelligence in general.

Rust

The most loved language of all is Rust, an open-source programming language that was hatched by tech pioneers at Mozilla in 2010.

In fact, Rust has been voted the most-loved language for the past four years in Stack Overflow’s annual developer surveys as solves pain points present in many other languages, providing a solid step forward with a limited number of downsides.

Rust concepts are also being used in Microsoft’s recently open-sourced Project Verona, an experimental language for safe infrastructure programming that could help Microsoft securely retain legacy C and C# code.
Mozilla Research describes Rust as a “systems programming language that focuses on speed, memory safety, and parallelism”.

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Researchers from Carnegie Mellon University have demonstrated how they can build intelligent tutoring systems. These systems are effective at teaching various subjects, including algebra and grammar. 

The researchers used a new method that relies on artificial intelligence in order to allow a teacher to teach a computer. The wording makes this method seem confusing, but think of it as a computer being taught how to teach by a human teacher. The computer can be taught by the human teacher showing it how to solve certain problems, such as multicolumn addition. If the computer gets the problem wrong, the teacher can correct it. 

Solving Problems On Its Own

One of the interesting parts of this method is that the computer system is able to not only teach and solve the problems how it was taught, but it can also solve all other problems in the topic by generalizing. This means that the computer can end up solving a problem outside of the ways the teacher taught it to. 

Daniel Weitekamp III is a Ph.D student in CMU’s Human-Computer Interaction Institute (HCII). 

“A student might learn one way to do a problem and that would be sufficient,” Weitekamp said. “But a tutoring system needs to learn every kind of way to solve a problem. It needs to learn how to teach problem solving, not just how to solve problems.”

The challenge that Weitekamp explains is one of the greatest in the development of AI-based tutoring systems. Newly developed intelligent tutoring systems can track student progress, help determine what to do next, and help students develop new skills by selecting effective practice problems. 

The Development of AI-Based Tutoring Systems

Ken Koedinger is a professor of human-computer interaction and psychology. Koedinger was one of the early developers of intelligent tutors, and working with others, production rules were programmed by hand. According to Koedinger, each hour of tutored instruction took 200 hours of development. Eventually, the group developed a more effective method, which demonstrated all of the possible ways to solve a problem. This took the 200 hours down to 40 or 50, but it is extremely difficult to demonstrate all of the possible solutions to some patterns. 

Koedinger has said that the new method could end up allowing a teacher to develop a 30-minute lesson in the same amount of time. 

“The only way to get to the full intelligent tutor up to now has been to write these AI rules,” Koedinger said. “But now the system is writing those rules.”

In the new method, a machine learning program is used to simulate the ways in which students learn. A teaching interface was created by Weitekamp, and it utilizes a “show-and-correct” process for programming.

While the method was demonstrated with multicolumn addition, the machine learning engine that is used can be applied to other subjects, such as equation solving, fraction addition, chemistry, English grammar, and science experiment environments. 

One of the main goals is for this method to allow teachers to construct their own computerized lessons, without the need of an AI programmer. This allows teachers to apply their own personal views on how to teach or which methods to use. 

Weitekamp, Koedinger, and HCII System Scientist Erik Harpstead authored the paper describing the method. It was accepted by the Conference of Human Factors in Computing Systems (CHI 2020). The conference was originally planned for this month, but the COVID-19 pandemic forced it to be canceled. The paper can now be found in the conference proceedings, located in the Association for Computing Machinery’s Digital Library.

The Institute of Education Sciences and Google helped support the research. 

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